server_url = 'https://s3.eu-central-1.amazonaws.com/avg-kitti/raw_data'
download_url = '{}/{}/{}.zip'.format(server_url, configs.foldername[:-5], configs.foldername)
download_and_unzip(configs.dataset_dir, download_url)
model = create_model(configs)
print('\n\n' + '-*=' * 30 + '\n\n')
assert os.path.isfile(configs.pretrained_path), "No file at {}".format(configs.pretrained_path)
model.load_state_dict(torch.load(configs.pretrained_path, map_location='cpu'))
print('Loaded weights from {}\n'.format(configs.pretrained_path))
configs.device = torch.device('cpu' if configs.no_cuda else 'cuda:{}'.format(configs.gpu_idx))
model = model.to(device=configs.device)
model.eval()
out_cap = None
demo_dataset = Demo_KittiDataset(configs)
with torch.no_grad():
for sample_idx in range(len(demo_dataset)):
metadatas, bev_map, img_rgb = demo_dataset.load_bevmap_front(sample_idx)
detections, bev_map, fps = do_detect(configs, model, bev_map, is_front=True)
# Draw prediction in the image
bev_map = (bev_map.permute(1, 2, 0).numpy() * 255).astype(np.uint8)
bev_map = cv2.resize(bev_map, (cnf.BEV_WIDTH, cnf.BEV_HEIGHT))
bev_map = draw_predictions(bev_map, detections, configs.num_classes)
# Rotate the bev_map
bev_map = cv2.rotate(bev_map, cv2.ROTATE_180)
img_path = metadatas['img_path'][0]
img_bgr = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2BGR)
server_url = 'https://s3.eu-central-1.amazonaws.com/avg-kitti/raw_data'
download_url = '{}/{}/{}.zip'.format(server_url, configs.foldername[:-5], configs.foldername)
download_and_unzip(configs.dataset_dir, download_url)
model = create_model(configs)
print('\n\n' + '-*=' * 30 + '\n\n')
assert os.path.isfile(configs.pretrained_path), "No file at {}".format(configs.pretrained_path)
model.load_state_dict(torch.load(configs.pretrained_path, map_location='cpu'))
print('Loaded weights from {}\n'.format(configs.pretrained_path))
configs.device = torch.device('cpu' if configs.no_cuda else 'cuda:{}'.format(configs.gpu_idx))
model = model.to(device=configs.device)
model.eval()
out_cap = None
demo_dataset = Demo_KittiDataset(configs)
with torch.no_grad():
for sample_idx in range(len(demo_dataset)):
metadatas, front_bevmap, back_bevmap, img_rgb = demo_dataset.load_bevmap_front_vs_back(sample_idx)
front_detections, front_bevmap, fps = do_detect(configs, model, front_bevmap, is_front=True)
back_detections, back_bevmap, _ = do_detect(configs, model, back_bevmap, is_front=False)
# Draw prediction in the image
front_bevmap = (front_bevmap.permute(1, 2, 0).numpy() * 255).astype(np.uint8)
front_bevmap = cv2.resize(front_bevmap, (cnf.BEV_WIDTH, cnf.BEV_HEIGHT))
front_bevmap = draw_predictions(front_bevmap, front_detections, configs.num_classes)
# Rotate the front_bevmap
front_bevmap = cv2.rotate(front_bevmap, cv2.ROTATE_90_COUNTERCLOCKWISE)
# Draw prediction in the image
back_bevmap = (back_bevmap.permute(1, 2, 0).numpy() * 255).astype(np.uint8)